公开(公告)号：US11531885B2

公开(公告)日：2022-12-20

申请号：US16658327

申请日：2019-10-21

Applicant: salesforce.com, inc.

Inventor： Michael Sollami ,  Yang Zhang

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06T3/40

Abstract: Systems, device and techniques are disclosed for training data generation for visual search model training. A catalog including catalog entries which may include images of an item and data about the item may be received. Labels may be applied to the images of the items based on the data about the items. The images of the items may be sorted into clusters using cluster analysis on the labels. Each cluster may include labels as categories of the cluster. Additional images may be received based on searching for the categories. Generative adversarial network (GAN) training data sets may be generated from the images of the items, the additional images, and the categories. GANs may be trained with the GAN training data sets. The GANs may generate images including images of generated items, which may be replaced with images of items from the catalog entries to create feature model training images.

公开(公告)号：US11507989B2

公开(公告)日：2022-11-22

申请号：US16658315

申请日：2019-10-21

Applicant: salesforce.com, inc.

Inventor： Michael Sollami ,  Yang Zhang

IPC: G06K9/62 ,  G06Q30/06 ,  G06N20/00 ,  G06F17/18 ,  G06N3/02

Abstract: Systems, device and techniques are disclosed for multi-label product categorization. A catalog entry and a list of categories may be received. The catalog entry may be associated with an item. A textual description may be generated by comparing words in the catalog entry to existing vocabularies of words and applying part-of-speech tagging to the catalog entry. A feature vector may be generated from the textual description by applying any of token frequency feature creation, term frequency-inverse document frequency feature creation, and pre-trained word embeddings to the textual description. A set of probabilities may be determined by inputting the feature vector into a machine learning model. The set of probabilities may include a probability for each category in the list of categories.